{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/particle-tracking-velocimetry-in-liquid","title":"Particle tracking velocimetry in liquid gallium flow about a cylindrical obstacle","arxiv_id":"2109.10699","date":"2021-09-17","proceeding":null,"authors":["Mihails Birjukovs","Peteris Zvejnieks","Tobias Lappan","Martins Sarma","Sascha Heitkam","Pavel Trtik","David Mannes","Sven Eckert","Andris Jakovics"],"abstract":"This paper demonstrates particle tracking velocimetry performed for a model system wherein particle-laden liquid metal flow about a cylindrical obstacle was studied. We present the image processing methodology developed for particle detection in images with disparate and often low signal- and contrast-to-noise ratios, and the application of our MHT-X tracing algorithm for particle trajectory reconstruction within and about the wake flow of the obstacle. Preliminary results indicate that the utilized methods enable consistent particle detection and recovery of long, representative particle trajectories with high confidence. However, we also underline the necessity of implementing a more advanced particle position extrapolation approach for increased tracking accuracy. We also show that the utilized particles exhibit the Stokes number range that suggests good flow tracking accuracy, and that the particle time scales extracted from reconstructed trajectories are consistent with expectations based a priori estimates.","url_abs":"https://arxiv.org/abs/2109.10699v1","url_pdf":"https://arxiv.org/pdf/2109.10699v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"particle-tracking-velocimetry-in-liquid","repo_url":"https://github.com/Mihails-Birjukovs/Low_C-SNR_Particle_Detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}